Predicting the Effectiveness of Covid-19 Vaccines from SARS-CoV-2 Variants Neutralisation Data
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Abstract
Rapid and accurate prediction of Covid-19 vaccine effectiveness is crucial to response against SARS-CoV-2 variants of concern. Despite intensive research, several prediction tasks are not well supported, such as predicting effectiveness of partial vaccination, of vaccine boosters and in vaccinated subpopulations. This paper introduces a novel predictive framework to accommodate such tasks and improve prediction accuracy. It was developed for predicting the symptomatic effectiveness of the BNT162b2 (Comirnaty) and ChAdOx1 nCoV-19 (Vaxzevria) vaccines but could apply to other vaccines and effectiveness types. Direct prediction within the framework uses levels of vaccine-induced neutralising antibodies against SARS-CoV-2 variants to fit efficacy and effectiveness estimates from studies with a given vaccine. Indirect prediction uses a model fitted for one vaccine to predict the effectiveness of another. The directly predicted effectiveness of Comirnaty against the Delta variant was 44.8% (22, 69) after one and 84.6% (64, 97) after two doses, which is close to 45.6% and 85.5%, the average estimates from effectiveness studies with the vaccine. The corresponding direct predictions for Vaxzevria were 41.6% (18, 68) and 63.2% (37, 86); and the indirect predictions, from the model fitted to Comirnaty data, were 45.5% (23, 70) and 61.2% (37, 83). Both sets of predictions are comparable to the average estimates, 42.5% and 66.3%, from effectiveness studies with Vaxzevria. Further results are presented on age subgroups; prediction biases and their mitigation; and implications for vaccination policies.
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SciScore for 10.1101/2021.09.06.21263160: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this …
SciScore for 10.1101/2021.09.06.21263160: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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